219 results about "Constant false alarm rate" patented technology

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.

The invention provides an adaptive CFAR detection method based on scene classification, and the method comprises the steps: carrying out the scene classification of an input SAR image through employing a K-means clustering algorithm, carrying out the target detection of a pure sea area obtained through classification through employing VI-CFAR, and carrying out the CFAR detection of a coastal area through employing a significance model. According to the method, the problems that a traditional CFAR algorithm is high in false alarm rate and unstable in detection performance in port and land complex backgrounds are effectively solved, detection parameters are optimized according to the complexity of different scene backgrounds, and therefore it is ensured that the CFAR algorithm can still maintain the constant false alarm rate in the sea clutter background, and the robustness of target detection is remarkably improved. The ocean area and the land area in the image are effectively distinguished, and the land area is shielded. According to the technical scheme, the problem that a traditional CFAR detection algorithm is interfered by a complex land background when detecting ships on the sea surface is solved.

The invention relates to an unmanned aerial vehicle detection method and system based on pilot frequency synchronization sequence matching, and belongs to the technical field of unmanned aerial vehicle detection. Receiving a to-be-detected signal, and intercepting a signal segment of which the length is a preset point number from the to-be-detected signal; performing fast Fourier transform on the signal segment to obtain frequency domain representation of the to-be-detected signal; performing fast Fourier transform on the general reference sequence according to a preset point number to obtain a frequency domain general reference sequence; performing conjugate operation on the frequency domain general reference sequence to obtain a frequency domain reference signal; performing convolution operation on the frequency domain representation of the signal to be detected and the frequency domain reference signal to obtain a frequency domain convolution result; and performing constant false alarm rate detection on the frequency domain convolution result to obtain an unmanned aerial vehicle detection result. According to the invention, the requirement of rapid and efficient detection of the unmanned aerial vehicle in a complex signal environment can be met.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention discloses a speed ambiguity resolution method suitable for 4D millimeter wave radar imaging. The method comprises the steps of 1, acquiring radar original target sampling data, and performing two-dimensional fast Fourier transform processing to obtain a target distance-Doppler matrix; carrying out constant false alarm rate detection on the target distance-Doppler matrix; 2, searching an optimal representative point as a speed representative point of the target; 3, adopting a hypothetical phase compensation speed ambiguity resolution algorithm for the speed representative point of the target to obtain a speed and a phase compensation value corresponding to the representative point; step 4, compensating all target points with phase compensation values calculated according to the representative points, and performing DOA estimation to obtain azimuth angle values; 5, coordinate transformation is carried out according to the obtained target point speed and azimuth angle value, the real position of the target under the three-dimensional coordinate system is obtained, and the representative point speed represents the real speed of the target. According to the invention, the detection capability of the MIMO radar imaging system on a high-speed target can be improved.

The invention relates to the technical field of radar signal processing, and discloses a complex ground reflection environment-oriented dual-path target detection method, which comprises the following steps of: performing quantile truncation and discretization processing on original range profile data, mapping a processed signal to a D-channel discrete feature space and generating a D-dimensional discrete feature matrix; the D-dimensional discrete feature matrix is input into a dual-channel parallel processing architecture, a CFAR adaptive detection module is adopted in a first path to suppress strong clutters to generate a first response matrix, and a false attention mechanism is adopted in a second path to enhance weak targets to generate a second response matrix; performing spatial alignment and weighting processing on the first response matrix and the second response matrix, and performing cross-domain confidence fusion to output a comprehensive response matrix; and carrying out fixed threshold binarization processing on the comprehensive response matrix based on a global threshold to obtain a target detection result. The method has both local gain compensation and global adaptive suppression, and can realize more robust detection of a weak target under a complex background.

The invention relates to the technical field of radar signal processing, and particularly provides a radar target detection constant false alarm rate control method and system based on sample quantile characteristics, a terminal and a storage medium, and the method comprises the steps: obtaining a radar echo signal of a to-be-detected unit; the method comprises the steps of grouping radar echo signals of a to-be-detected unit, constructing a sample set, and extracting quantile features from grouped samples according to a plurality of preset quantile levels to obtain quantile feature vectors; based on the quantile feature vector, calculating to obtain a statistic of the to-be-tested unit; comparing the statistical magnitude of the to-be-detected unit with a preset judgment threshold value, and if the statistical magnitude is greater than the judgment threshold value, judging that a target exists in the to-be-detected unit; and if the statistical magnitude is smaller than or equal to the judgment threshold value, judging that no target exists in the to-be-detected unit. According to the invention, the target detection sensitivity and the anti-clutter capability are significantly improved.

The invention provides an ultrasonic three-dimensional imaging method and system based on an FPGA, and relates to the technical field of ultrasonic imaging, and the method comprises the steps: transmitting a chirp signal, receiving an echo signal reflected by a target object, and carrying out the preprocessing; carrying out adaptive filtering, constant false alarm rate detection, adaptive beam forming and three-dimensional positioning algorithm processing on the preprocessed signal to generate target position data; and performing three-dimensional point cloud image reconstruction on the target position data by using a density-based spatial clustering algorithm and a deep learning model, and outputting a target three-dimensional image. Through the innovation of a signal processing algorithm and the collaborative design of the system, the technical bottlenecks of a traditional ultrasonic sensor in detection distance, anti-interference, real-time performance and positioning precision are solved, and the three-dimensional sensing capability with high reliability and strong environmental adaptability is provided for intelligent driving.

The present invention discloses a radar CFAR target detection method based on the prefix sum algorithm. The method performs Fourier transform and square-law detection on the radar echo signal to obtain the range-Doppler matrix A, and performs constant false alarm rate (CFAR) processing on the range-Doppler matrix A to determine whether a target exists. When performing CFAR detection, a rectangular reference sliding window is used. First, the prefix sum algorithm is used to obtain the prefix sum matrix SUM of the range-Doppler matrix A, and then the average value of the data of the surrounding reference cells of the cell a to be measured is calculated through the prefix sum matrix. The average value is multiplied by a coefficient to obtain an estimated value of the background clutter, and the estimated value is compared with the cell to be detected to determine whether the cell to be detected is a target. The method of the present invention ensures the accuracy of detection while reducing the complexity of the algorithm, reducing the computational amount of the original CFAR detection, and improving the detection efficiency. i,j The method of the present invention ensures the accuracy of detection while reducing the complexity of the algorithm, reducing the computational amount of the original CFAR detection, and improving the detection efficiency.

A non-contact real-time monitoring system of physiological signs based on millimeter-wave radar includes a millimeter-wave radar and multiple modules for processing radar signals. The millimeter-wave radar is configured to continuously transmit electromagnetic wave signals and simultaneously receive echo signals, perform frequency mixing processing on the echo signals to obtain an intermediate frequency signal, and process the intermediate frequency signal to obtain a radar four-dimensional data matrix. Human body physiological signs are monitored by analyzing body thoracic cavity micro-motion information in signals through the modules; a target echo is processed by adopting a constant false alarm rate detection algorithm, and invalid signals are filtered. A self-adaptive range cell selection algorithm based on short-time stability of respiratory signals is adopted to capture radar echoes reflecting physiological movement. Mixed human body physiological sign signals are processed by using a VMD algorithm, and key parameters in VMD are optimized by using a GWO algorithm.

The invention provides an underwater target echo detection method and system based on amplitude fluctuation characteristics, and the method comprises the steps: S1, building a target detection model according to a preset rule; s2, acquiring an echo image, performing sliding window processing on waveform data in the echo image, and establishing a probability density function model; s3, calculating a likelihood ratio function of the waveform data according to the target detection model and the probability density function model; s4, calculating a threshold value according to the probability density function model and the likelihood ratio function; and S5, updating the echo image by using a threshold value as a criterion to obtain a target detection result. According to the invention, through constant false alarm rate detection and the Neyman-Pearson criterion, a dynamic threshold value detection method which fluctuates along with the target intensity and changes along with the background is provided, the echo image is detected, and a new echo image is formed.

The invention discloses a clustering algorithm for Terahertz radar extended target tracking. The clustering algorithm comprises three steps of drawing an azimuth distance map by using original data, carrying out two-dimensional unit average constant false alarm rate detection on an azimuth distance dimension, and clustering by a connected domain labeling method. In the first step, a grid interpolation method is used for carrying out zero interpolation on the reflection intensity of discrete points, so that subsequent calculation of background noise is facilitated; in the second step, a threshold value is calculated by setting related parameters, and part of clutter interference is filtered out; in the third step, adjacent data points are communicated through an eight-field method, then morphological closed operation is designed to merge adjacent targets, the problem of excessive segmentation is effectively avoided, then a minimum area threshold value is added to filter clutter interference, effective targets are reserved, and finally the clustering effect is more remarkable through target center point labeling. The clustering algorithm for Terahertz radar extended target tracking is low in cost, high in processing speed and remarkable in effect, and has certain application value in the fields of Terahertz radar extended target tracking and the like.

The invention relates to the technical field of urban low-altitude safety monitoring, and discloses a low-altitude economic airspace passive safety early warning method and system. The method comprises the following steps: collecting multi-modal observation data streams of a target at different moments; generating multi-modal frame data with a unified timestamp; establishing a spatial confidence distribution map; extracting fusion discrimination features; executing multi-channel small target detection; continuously tracking the preliminary target; and introducing a constant false alarm rate framework to set a dynamic confidence threshold to execute alarm judgment. Compared with the prior art, the technical problem that high-confidence all-weather passive monitoring cannot be realized in urban core areas with dense buildings, serious signal shielding and easy GPS failure due to dependence on single vision or radar information in the prior art is solved. According to the method, the space confidence distribution diagram and the dynamic constant false alarm mechanism based on the shielding diagram are constructed by fusing the multi-modal sensing data, so that the safety early warning of the low-altitude small aircraft is realized, and the passive safety guarantee capability of the low-altitude economic airspace is improved.

The invention discloses an improved self-adaptive CFAR (Constant False Alarm Rate) detection method for defects of worsted wool fabric, and relates to the technical field of defect detection of worsted wool fabric. The invention relates to an improved self-adaptive CFAR (Constant False Alarm Rate) detection method for defects of worsted wool fabric. The method comprises the following steps: shooting a color image of the worsted wool fabric by adopting a CCD (Charge Coupled Device) camera; converting the acquired color image into a grayscale image to obtain a grayscale value of each pixel; traversing the target grayscale image pixel by pixel by using a sliding window type CFAR detector; wherein the sliding window comprises a front edge sliding window and a rear edge sliding window; the combination of the leading edge sliding window and the trailing edge sliding window is used as a reference unit. According to the scheme, through the double-sliding-window structure design and the mean value self-adaptive selection algorithm, the false detection problem of the light and shade junction area is accurately restrained, and the false detection rate is remarkably reduced; the technical bottleneck that the false detection rate is high due to the fact that the fabric is uneven is broken through, double optimization of the false detection rate and the detection rate is achieved in a complex industrial scene, and a reliable technical scheme is provided for intelligent quality inspection of the worsted fabric.

The invention discloses an unmanned aerial vehicle spectrum anomaly detection and trajectory generation method based on unsupervised learning, and the method comprises the steps: continuously collecting broadband radio frequency signals through an SDR, and obtaining an IQ data stream; performing short-time Fourier transform to obtain a time-frequency spectrogram; performing robust normalization processing on the time-frequency spectrogram to obtain a standardized spectrogram after slow-varying drift suppression; performing constant false alarm rate detection, and generating a primary abnormal candidate binary mask; identifying spectrogram sub-blocks corresponding to the primary abnormal candidate binary masks from the standardized spectrogram, and inputting the spectrogram sub-blocks into a pre-trained unsupervised deep learning model to obtain an abnormal scoring graph; fusing with the primary abnormal candidate binary mask to generate an abnormal heat map; and clustering the abnormal heat map in a time-frequency dimension, carrying out cross-time window trajectory association and filtering on a cluster, and outputting a time-frequency trajectory of the unmanned aerial vehicle signal. According to the invention, the reliability of anomaly detection is enhanced, the risk of false detection and missing detection is reduced, and the continuity and accuracy of track generation are guaranteed.

The invention discloses a generalized clutter texture distribution-oriented extended target full-adaptive constant false alarm rate detection method, which belongs to the field of radar signal processing, and comprises the following steps of: constructing test statistics according to texture distribution knowledge, covariance matrix structure information, signal subspace and the like; establishing an interpolation function of a threshold offline by using a Monte Carlo method, and estimating a corresponding threshold quickly and precisely according to parameters of a probability density function of a texture component; in a radar target detection process, unknown parameters in texture distribution are estimated by using a second group of training data, a full-adaptive detector is constructed by combining an interpolation function of test statistics and a threshold, and the detector is ensured to have a constant false alarm rate property for the parameters of the texture distribution and the change of a clutter covariance matrix. Compared with a traditional radar target detection method, the cognitive ability of a radar system to a complex environment is remarkably improved, and the method has good engineering application value and popularization potential.

The invention provides a target detection method and device based on a millimeter wave radar, and a control unit, and relates to the technical field of radar signal processing. The method comprises the steps that a first thermodynamic diagram corresponding to a detection area of the millimeter wave radar is acquired, the first thermodynamic diagram is divided at equal intervals in the speed dimension, multiple sub-bands are obtained, and the multiple sub-bands comprise a non-empty band and at least two empty bands; performing fusion processing on the plurality of sub-bands to obtain a second thermodynamic diagram; based on a constant false alarm rate algorithm, according to the second thermodynamic diagram, determining a detection point trace corresponding to a real target in the targets; and determining the actual distance and the actual speed of the real target relative to the millimeter wave radar based on the detection trace and according to the phase offset of the transmitting antenna. According to the method, energy in the sub-bands is accumulated in a sub-band segmentation and fusion mode, so that the spectrum peak value of the real target is more prominent, the signal-to-noise ratio of the real target is effectively improved, the probability of occurrence of a false target is reduced, and the target detection precision is improved.

Disclosed in the present invention are a method for eliminating a one-bit signal harmonic false target and a related component. In the method, dechirping, a frequency shift, one-bit quantization, AD sampling, and a Fourier transform are performed on an echo signal to obtain a frequency spectrum, and then constant false alarm rate detection is performed on the frequency spectrum, to obtain a distance of a real target and a distance of a false target. For the distance of the false target, a difference comparison is performed on multiple snapshot-based angle of arrival information and single snapshot-based angle of arrival information, a distance corresponding to an angle with the smallest angle difference is obtained from the single snapshot-based angle of arrival information, and the distance is used as the distance of the real target, thereby eliminating the false target and then reducing costs of data acquisition, transmission, storage, and processing.

The invention provides a constant false alarm target detection method and device for an airborne random pulse repetition interval radar system, and relates to the technical field of radar signal processing. The method comprises the following steps: estimating a target scene through a compressed sensing model according to an echo sampling result and an observation matrix to obtain an estimation result of the target scene; taking a constant detected false alarm rate as a target, and constructing test statistics according to an echo sampling result, the observation matrix and an estimation result; performing threshold calculation based on chi-square distribution according to a preset expected false alarm rate and the test statistics to obtain a detection threshold; based on the support set definition of the clutter region, calculating according to the information of the receiving platform to obtain the position of the clutter region; and based on the clutter region position, according to the test statistics and the detection threshold, performing target position detection on a region outside the clutter region to obtain a target position. The method is an efficient and accurate constant false alarm target detection method for an airborne random pulse repetition interval radar.

The invention discloses an active sonar target identification method based on message passing multi-hypothesis tracking, and belongs to the technical field of underwater target detection. The method comprises the following steps: firstly, processing single-cycle sonar data by using adaptive constant false alarm detection, obtaining observation points, and forming an effective observation set through hierarchical clustering; thirdly, extrapolating the existing target, calculating the association probability of the existing target and the current observation by adopting a message passing method, and generating a plurality of groups of association hypotheses; then, the target state is corrected through Kalman filtering, target rejection and new management are executed, and a high-probability hypothesis is reserved; and finally, estimating the motion speed based on the target multi-period position sequence, and distinguishing a static object from a suspicious motion target according to a speed threshold. Through the multi-hypothesis tracking and probability message passing mechanism, clutter interference and static target influence in a complex underwater environment are effectively overcome, and the stability of target tracking, the accuracy of speed estimation and the overall reliability of system identification are remarkably improved.

The invention relates to the field of signal processing, deep learning and radar target detection, in particular to a radar target detection method based on shallow and deep feature fusion. By fusing the shallow multi-domain features and the deep learning features, the problem of poor detection generalization caused by dependence on shallow features in the prior art is solved. The shallow features are subjected to multi-domain analysis, weighting and normalization processing, so that the distinguishing capability of target echoes and sea clutters is enhanced; the deep layer features are extracted through 1D-ResNet50 and spliced with the shallow layer features, the physical significance and the expression ability are achieved, and the feature representation ability of the model is improved. And in combination with a constant false alarm rate algorithm, a detection threshold value is dynamically adjusted, and the stability and robustness in a complex marine environment are ensured. According to the method, the precision, generalization and practicability of target detection are remarkably improved, and the method has high engineering application value.

The invention discloses an intermittent sampling and forwarding interference suppression method based on phase coding pulse train signal processing, and relates to the field of data processing, and the method comprises the steps: processing a power distribution function of an RD frequency spectrum to obtain a distance-Doppler matrix only containing target information, eliminating a true target protection region, and obtaining a distance-Doppler matrix containing target information; a two-dimensional switching constant false alarm rate detection algorithm is adopted to detect the distance-Doppler matrix only containing the false target, and delay coordinates of the false target are extracted; the delay coordinates of the false targets are clustered through a mean shift algorithm, a window function is constructed according to the clustering center of the false targets, the two-dimensional S-CFAR detection result is processed, the false targets are removed, and a rough detection result of the true targets is obtained; doppler vector spaces of a false target and a true target are constructed, an oblique projection operator is obtained, Doppler filtering is carried out, and a one-dimensional pulse compression result only containing a final detection result of the true target is obtained. According to the invention, fine detection when clutter or interference power is relatively high can be realized.

An indoor optimization positioning method of an MIMO frequency modulation continuous wave radar comprises the following steps: after a data matrix is generated based on an echo signal after analog-to-digital conversion, filtering an environment static target and radio frequency leakage by using a time domain filtering technology, and after the data matrix after the time domain filtering is carried out, carrying out indoor optimization positioning on the MIMO frequency modulation continuous wave radar based on the data matrix after the time domain filtering; the actual distance between the target and the radar is calculated through distance dimension fast Fourier transform, the speed Doppler of the target relative to the radar is calculated through speed fast Fourier transform, so that a distance Doppler spectrum is generated, after the signal-to-noise ratio is increased through multi-channel data incoherent accumulation, the total number of the targets is estimated through a constant false alarm rate (CFAR) algorithm, and the distance Doppler spectrum is obtained. Obtaining distances and speeds corresponding to different target points; and finally, angle estimation is carried out on each point on the CFAR through a super-resolution solution angle algorithm (ESPRIT) to obtain angle information, distance, speed and angle data matching is carried out through digital beam forming (DBF), distance, angle and speed data of a corresponding target are obtained, and a two-dimensional coordinate of the target is correspondingly obtained. According to the invention, the number of human body target point clouds can be increased while human body coordinates, speed and angle information are accurately obtained, and the method has the advantages of high interference resistance, low requirement on the signal-to-noise ratio of data and low dependence degree on the external environment.

The invention relates to the technical field of intelligent sensors, in particular to an unmanned aerial vehicle track intelligent tracking method based on a millimeter-wave radar, which comprises the following steps: receiving echo signals collected by the front end of the millimeter-wave radar, carrying out constant false alarm rate detection and Doppler frequency shift extraction on the echo signals, generating an unmanned aerial vehicle target space coordinate set, and carrying out target tracking on the unmanned aerial vehicle. And mapping the target space coordinate set of the unmanned aerial vehicle to a three-dimensional Euclidean space. According to the method, a non-convex optimization objective function containing beam switching time cost and precision constraint is constructed, and mathematical optimal solution of radar beam resources is realized. The generated adaptive scheduling instruction is used for adjusting the pulse repetition frequency and the beam residence time, it is ensured that radar front-end parameters dynamically evolve along with the target state, overall centroid tracking is emphasized for rigid body formation so as to save resources, independent high-frequency refreshing is emphasized for discrete targets so as to ensure the precision, and the accuracy is improved. And the continuity and robustness of trajectory tracking of the unmanned aerial vehicle in a complex electromagnetic environment are improved.

The present disclosure provides a constant false alarm rate-based downhole casing nondestructive testing method, device and medium. The method can include: obtaining a detection threshold according to a preset false alarm probability and background noise power of a target pulsed eddy current array detection signal, wherein the false alarm probability refers to the probability that the detection system incorrectly determines the presence of a damaged target when a non-damaged target is present in the downhole casing; screening the target pulsed eddy current array detection signal based on the detection threshold to obtain a downhole casing damage detection result signal; and determining the position of the downhole casing damage according to the downhole casing damage detection result signal. Through this technical solution, the detection threshold can be dynamically adjusted, and the position of the downhole casing damage can be accurately located, thereby improving the efficiency and accuracy of downhole casing damage detection.

The invention discloses an anti-high dynamic Doppler low signal-to-noise ratio satellite signal blind detection method, which comprises the following steps of: performing frequency domain analysis on a delay correlation signal according to a signal symbol rate, and dividing a signal band and a noise band near the delay correlation signal to construct a detection quantity to perform constant false alarm rate (CFAR) detection; and finally, obtaining a final signal existence or disappearance result through time consistency judgment. According to the method disclosed by the invention, the analytic CFAR threshold and the state machine smoothing are combined, and the problem of detection failure in a low signal-to-noise ratio and high dynamic Doppler environment in the prior art is solved.

The invention relates to a multichannel real-time processing method and device for ultrahigh frequency river detection radar signals. The method comprises the following steps: synchronously sampling analog echo data of multiple channels to obtain a digital echo signal; based on the digital echo data, pulse compression processing is carried out, and baseband multi-channel data is output; performing memory scheduling processing on the baseband multichannel data based on a ping-pong buffer strategy to obtain continuous data blocks; based on a preset adaptive filtering weight, performing partition adaptive weighted summation filtering on the continuous data blocks to generate a clutter suppressed single-channel data matrix; performing constant false alarm rate detection based on the single-channel data matrix to obtain a target detection list; and based on the target detection list, performing visual rendering, and generating a radar detection report of the river detection radar for detection. By adopting the method, clutters can be effectively suppressed, and the credibility of the river detection radar signal is improved.

The application provides a hypersonic platform radar target detection method, device and electronic equipment, relates to the technical field of hypersonic radar target detection, and the method comprises the following steps: inputting a received first echo signal into a time domain echo signal model, converting the first echo signal from a first signal domain to a second signal domain by using the time domain echo signal model, and obtaining a second echo signal; filtering the second echo signal by using an optimal parameter filter, obtaining a filtered third echo signal, converting the third echo signal into a third signal domain, and obtaining a coherent accumulation result; determining whether a target is detected by using a constant false alarm rate detection based on the coherent accumulation result, and determining the distance of the target based on the position of a peak value in the accumulation result in the case that it is determined that the target is detected. The hypersonic platform radar target detection method, device and electronic equipment provided by the application can realize high-precision, high-efficiency and highly adaptable radar target detection.

A sensor and method for sleep position detection including: a transmitter configured to transmit electromagnetic waves between 30 GHz and 300 GHz; a receiver configured to receive the electromagnetic waves from the transmitter, wherein the transmitter and receiver are positioned in relation to person sleeping such that the receiver receives reflected electromagnetic waves; and a control station configured to analyze the transmitted and received electromagnetic waves to determine a position of the person sleeping. In some cases, the method may include: forming a radar cube of results; performing a fast fourier transform (FFT) on the radar cube; applying a constant false alarm rate (CFAR) processor to the FFT data; determining a capon gradient; forming a 5-dimensional feature space based on the capon gradient; and conducting an optimization of SVM.

The present invention belongs to the technical field of maritime target detection, and particularly relates to a two-dimensional constant false alarm target detection method and detector based on dual-observation-channel data. The method includes: obtaining the echo time series transmitted on each observation channel and received on each range cell; the observation channels include two observation channels of the cell to be detected and two observation channels of the reference cell; respectively performing square-law detection on the received echo time series to obtain detection statistics; calculating the estimated values of the clutter power levels of the two observation channels respectively based on the samples after square-law detection of the echo data of the reference cell; calculating the normalization factor according to the preset false alarm probability; and performing target detection according to the detection statistics, the estimated values of the clutter power levels, and the normalization factor, in combination with the two-dimensional constant false alarm adaptive decision criterion. It reduces the missed detection or misjudgment caused by the limitation of single-channel information, and improves the accuracy of judging the presence or absence of a target.